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#!/usr/bin/python
################################################################################
# Description
################################################################################
# This scriipt demonstrates the visualizer submodule.
# This script should be invoked with the names of the trace files you want to
# use. For example, `gedf_test.py ../sample_traces/*.bin`.
# You probably want to redirect the (normally lengthy) output to a file!
# Otherwise, speed is greatly decreased b/c terminal printing is slow.
################################################################################
# Setup
################################################################################
# Import the modules we need. You should not need to know about
# their internals.
import unit_trace
from unit_trace import trace_reader
from unit_trace import sanitizer
from unit_trace import gedf_test
from unit_trace import stats
from unit_trace import stdout_printer
from unit_trace import visualizer
# Get trace files from command line arguments
from optparse import OptionParser
parser = OptionParser()
(options, traces) = parser.parse_args()
traces = list(traces)
################################################################################
# Pipeline
################################################################################
# Read events from traces
stream = trace_reader.trace_reader(traces)
# Filter out garbage events
stream = sanitizer.sanitizer(stream)
# Produce G-EDF error records
stream = gedf_test.gedf_test(stream)
# Produce a statistics record
stream = stats.stats(stream)
# Filter some records out
def my_filter(record):
if record.record_type == 'error' and record.type_name == 'inversion_end':
if record.job.inversion_end - record.job.inversion_start < 4000000:
return False
return True
#stream = filter(my_filter, stream)
# It is possible to split the stream in two,
# so we can use more than one output mechanism (for example)
# without re-doing earlier work.
# import itertools
# stream1, stream2 = itertools.tee(stream,2)
# Print the records to stdout
visualizer.visualizer(stream)
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